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Intro to Data Engineering
Data Engineering Undercurrents
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Intro to Data Engineering
Overview
Lifecycle
Undercurrents
Data Architecture
Right Technologies
Requirements Gathering
Data Pipelines
Source Systems
Data Ingestion
On this page
Overview
Security
Data Management
Data Architecture
DataOps
Orchestration
Software Engineering
Key Takeaways
Intro to Data Engineering
Data Engineering Undercurrents
​
Overview
Undercurrents
:
Practices that apply across the entire data engineering lifecycle:
Security
Data Management
Data Architecture
DataOps
Orchestration
Software Engineering
​
Security
Core Principle
: Protect sensitive data (e.g., personal, proprietary).
Key Practices
:
Principle of Least Privilege
: Grant users/applications only the access they need.
Data Sensitivity
: Avoid ingesting sensitive data unless absolutely necessary.
Cloud Security
: Understand
IAM (Identity and Access Management)
, encryption, and networking protocols.
Cultural Aspect
:
Security is a
shared responsibility
across the organization.
Avoid
security theater
(superficial compliance without a true security culture).
Common Mistakes
:
Exposing S3 buckets or databases to the public internet.
Ignoring basic precautions like secure password sharing.
Key Takeaway
: Security is about
principles, protocols, and people
.
​
Data Management
Definition
: The development, execution, and supervision of plans to deliver, control, protect, and enhance the value of data.
DAMA (Data Management Association)
:
Provides the
Data Management Body of Knowledge (DMBOK)
.
Covers 11 knowledge areas, including
data governance
,
data modeling
, and
data integration
.
Data Governance
:
Ensures
data quality
,
integrity
,
security
, and
usability
.
Central to all other data management areas.
Data Quality
:
High-quality data is
accurate
,
complete
,
discoverable
, and
timely
.
Poor data quality leads to wasted time, poor decisions, and loss of trust.
Key Takeaway
: Data management ensures data is a
valuable business asset
.
​
Data Architecture
Definition
: The design of systems to support the evolving data needs of an enterprise through flexible and reversible decisions.
Key Principles
:
Choose Common Components Wisely
: Use components that facilitate collaboration.
Plan for Failure
: Design for both success and failure scenarios.
Architect for Scalability
: Build systems that scale up and down with demand.
Architecture is Leadership
: Think like an architect to lead and mentor others.
Always Be Architecting
: Continuously evolve systems to meet changing needs.
Build Loosely Coupled Systems
: Use interchangeable components for flexibility.
Make Reversible Decisions
: Ensure design choices can be easily changed.
Prioritize Security
: Apply security principles like least privilege and zero trust.
Embrace FinOps
: Optimize costs while maximizing revenue potential.
Key Takeaway
: Good data architecture is
flexible
,
scalable
, and
secure
.
​
DataOps
Definition
: A set of cultural habits and practices borrowed from
DevOps
to improve the development and quality of data products.
Key Pillars
:
Automation
:
Use
CI/CD (Continuous Integration/Continuous Delivery)
for data pipelines.
Automate tasks like ingestion, transformation, and serving.
Observability and Monitoring
:
Monitor pipelines to detect failures early.
Avoid bad data lingering in reports or dashboards.
Incident Response
:
Rapidly identify and resolve issues.
Foster open and blameless communication.
Key Takeaway
: DataOps improves
efficiency
,
quality
, and
reliability
of data systems.
​
Orchestration
Definition
: Coordinating and managing tasks in data pipelines.
Approaches
:
Manual Execution
: Useful for prototyping but not sustainable.
Pure Scheduling
: Automate tasks at specific times but lacks dependency management.
Orchestration Frameworks
:
Tools like
Apache Airflow
,
Dagster
,
Prefect
, and
Mage
.
Automate tasks with
dependencies
and
monitoring
.
Directed Acyclic Graphs (DAGs)
:
Represent data pipelines as flowcharts with
nodes
(tasks) and
edges
(dependencies).
Ensure data flows in one direction without loops.
Key Takeaway
: Orchestration frameworks automate and optimize data pipelines.
​
Software Engineering
Core Skill
: Write
clean
,
readable
,
testable
, and
deployable
code.
Languages and Frameworks
:
SQL
,
Python
,
Bash
,
Spark
,
Kafka
,
Java
,
Scala
,
Rust
,
Go
.
Key Areas
:
Data Processing
: Write code for ingestion, transformation, and serving.
Open Source Contributions
: Contribute to frameworks like Apache Airflow.
Infrastructure as Code
: Automate infrastructure setup using code.
Key Takeaway
: Strong software engineering skills are essential for
adding value
as a data engineer.
​
Key Takeaways
Undercurrents
:
Security, Data Management, DataOps, Data Architecture, Orchestration, and Software Engineering are foundational to data engineering.
Security
:
Protect data through
least privilege
,
encryption
, and a
security-first culture
.
Data Management
:
Ensure data is
high-quality
,
secure
, and
usable
through governance and best practices.
Data Architecture
:
Design
flexible
,
scalable
, and
secure
systems that evolve with business needs.
DataOps
:
Automate, monitor, and respond to incidents to improve
efficiency
and
reliability
.
Orchestration
:
Use frameworks like
Apache Airflow
to automate and manage complex data pipelines.
Software Engineering
:
Write
production-grade code
to build and maintain robust data systems.
Source
: DeepLearning.ai data engineering course.
Data Engineering Lifecycle
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Responses are generated using AI and may contain mistakes.